Standard syllabus
Linear algebra for CS · Undergraduate · CS / Programming
Topics
Core linear algebra
- Vectors in R^n; dot product, norms, and angles
- Matrices, matrix multiplication, and linear maps
- Systems of equations; Gaussian elimination
- Rank, null space, and column space
- Determinants and invertibility (computational view)
Eigenmethods
- Eigenvalues and eigenvectors
- Diagonalization and spectral theorem (symmetric case)
- Orthogonality, projections, and Gram–Schmidt
- Least squares and normal equations
- Singular value decomposition (intro)
CS-facing linear algebra
- Matrix representations of graphs and Markov chains
- Least squares for fitting and overdetermined systems
- Eigenconcepts for PageRank-style iterations (intro)
- SVD intuition for dimensionality reduction
- Orthogonality in projections and QR ideas
- Numerical stability awareness for floating-point ops
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$1,162 · Linear algebra for CS · 18 tutoring hrs
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